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Explainable artificial intelligence

XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.

Papers

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Showing 376–400 of 971 papers

TitleStatusHype
Towards a general framework for improving the performance of classifiers using XAI methods—0
Interpretable Machine Learning for Survival AnalysisCode0
Explainability through uncertainty: Trustworthy decision-making with neural networks—0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems—0
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization—0
A Survey of Explainable Knowledge Tracing—0
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning—0
People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AICode0
Explainable Learning with Gaussian ProcessesCode0
An Explainable AI Framework for Artificial Intelligence of Medical Things—0
Explainable AI for Embedded Systems Design: A Case Study of Static Redundant NVM Memory Write Prediction—0
XAI-Based Detection of Adversarial Attacks on Deepfake DetectorsCode0
AcME-AD: Accelerated Model Explanations for Anomaly Detection—0
Introducing User Feedback-based Counterfactual Explanations (UFCE)Code0
Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity—0
Position: Explain to Question not to Justify—0
MultiFIX: An XAI-friendly feature inducing approach to building models from multimodal data—0
Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive BiasCode0
Implementing local-explainability in Gradient Boosting Trees: Feature Contribution—0
Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence—0
You can monitor your hydration level using your smartphone camera—0
Beyond explaining: XAI-based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction—0
Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating—0
XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics—0
Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence—0
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